科研之友
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摘要
In wireless sensor networks, time division multiple access (TDMA)-based MAC can potentially reduce the delay and provide real-time guarantees as well as save power by eliminating collisions. In this kind of MAC, a common energy-saving strategy is to allow the sensors to turn their radio off when not engaged. However, too much state transitions between the active and sleep modes would also waste energy. In order to save this part of energy and further improve the time performance, a multi-objective TDMA scheduling problem for many-to-one sensor networks is presented. An effective optimization framework is then proposed, where genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are hybridized to enhance the searching ability. Simulation results with different network sizes are given. Three algorithms are used for comparisons, which are PSO algorithm, max degree first coloring algorithm and node based scheduling algorithm. It is shown that the proposed hybrid algorithm is superior over these three algorithms on a specified objective, which can be the total time or the total energy for data collection. In addition, the results reveal that the proposed optimization framework can flexibly deal with a multi-objective optimization problem.
